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What is Machine Learning?

Machine learning (ML) is an artificial intelligence branch where we define algorithms, with the aim of learning about a model that describes and extracts meaningful information from data.

Exciting applications of ML can be found in fields such as predictive maintenance in industrial environments, image analysis for medical applications, time series forecasting for finance and many other sectors, face detection and identification for security purposes, autonomous driving, text comprehension, speech recognition, recommendation systems, and many other applications of ML are countless, and we probably use them daily without even knowing it!

Just think about the camera application on your smartphone— when you open the app and you point the camera toward a person, you see a square around the person's face. How is this possible? For a computer, an...

Key benefits

Understand the basics of machine learning and discover the power of neural networks and deep learning

Explore the structure of the TensorFlow framework and understand how to transition to TF 2.0

Solve any deep learning problem by developing neural network-based solutions using TF 2.0

Description

TensorFlow, the most popular and widely used machine learning framework, has made it possible for almost anyone to develop machine learning solutions with ease. With TensorFlow (TF) 2.0, you'll explore a revamped framework structure, offering a wide variety of new features aimed at improving productivity and ease of use for developers. This book covers machine learning with a focus on developing neural network-based solutions. You'll start by getting familiar with the concepts and techniques required to build solutions to deep learning problems. As you advance, you’ll learn how to create classifiers, build object detection and semantic segmentation networks, train generative models, and speed up the development process using TF 2.0 tools such as TensorFlow Datasets and TensorFlow Hub. By the end of this TensorFlow book, you'll be ready to solve any machine learning problem by developing solutions using TF 2.0 and putting them into production.

Who is this book for?

If you're a developer who wants to get started with machine learning and TensorFlow, or a data scientist interested in developing neural network solutions in TF 2.0, this book is for you. Experienced machine learning engineers who want to master the new features of the TensorFlow framework will also find this book useful. Basic knowledge of calculus and a strong understanding of Python programming will help you grasp the topics covered in this book.

What you will learn

Grasp machine learning and neural network techniques to solve challenging tasks

Apply the new features of TF 2.0 to speed up development

Use TensorFlow Datasets (tfds) and the tf.data API to build high-efficiency data input pipelines

Perform transfer learning and fine-tuning with TensorFlow Hub

Define and train networks to solve object detection and semantic segmentation problems

Train Generative Adversarial Networks (GANs) to generate images and data distributions

Use the SavedModel file format to put a model, or a generic computational graph, into production

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

€41.99

Hands-On Neural Networks with TensorFlow 2.0

€32.99

Hands-On Computer Vision with TensorFlow 2

€29.99

Total € 104.97

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Aundrea Mar 12, 2021

The book is very pragmatic and that is why I recommend it. It has many eye opening examples on how to use TensorFlow 2.0. It is a great resource if you really want to understand neural networks and deep learning. I have other books on machine learning and TensorFlow but none of them explain things as clearly as this book.

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fasttouch Nov 17, 2019

This is a great book on TensorFlow 2.0. All the book is well written and explain the concepts really well. It is extremely precise and goes really deep in technical details. Especially Chapter 3 and 4 are fantastic. If you want to know how TensorFlow works this book is for you.

joe hoeller Jan 19, 2020

Excellent in-depth resource for beginners that have never been exposed to Deep Learning in TensorFlow 2.0, with some of the material extending upon that and taking the beginner to an intermediate level.I recommend coding examples from hand while creating notes above the code line for line that summarize in 1-2 sentences what each line of code does.This will reinforce what you learn while developing familiarity and experience with the Tensorflow version 2.0 API.Well written and executed for this level and within the context of deep learning and neural networks.

Grigory Sapunov Jan 08, 2020

This is a good introductory book to start with TF 1.0 and 2.0. The book is not too general as I thought from some reviews, it contains a lot of relevant topics required to meaningfully start doing ML/DL projects. Just look at the contents. For example, it has an intro into ML itself, enough deep learning basics, all the relevant TensorFlow stuff, highlighting the differences between 1.0 and 2.0, explains new stuff like TF Hub, describes all relevant metrics for different tasks and so on. Examples range from basic image classification to more advanced object detection and even semantic segmentation, not forgetting a basic intro into GANs. So, I like it. It's definitely not a typical book about nothing, it's a good introductory book to quickly start learning this field. It will save you a lot of time comparing to reading different pages and posts in the internet.

Placeholder Jan 02, 2020

This books explains end to end new features added in TF2. Using Gradient Tape, writing custom training loop. I

Hands-On Neural Networks with TensorFlow 2.0: Understand TensorFlow, from static graph to eager execution, and design neural networks

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Hands-On Neural Networks with TensorFlow 2.0

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Hands-On Neural Networks with TensorFlow 2.0: Understand TensorFlow, from static graph to eager execution, and design neural networks

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